Technology Integration in Next Generation Mobile Learning

Laine, Teemu H.; Nygren, Eeva

doi:10.4324/9780203076095-6

Cited by 2 publications

(3 citation statements)

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“…Context-awareness is defined as the process of detecting and analyzing context entities by various devices and analytical methods (Laine & Nygren, 2015). The context is multifaceted.…”

Section: Literature Reviewmentioning

confidence: 99%

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Learning preference: development in smart learning environments

Zhang

Hung

et al. 2021

IDD

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Purpose The purpose of this study is to conduct a systematic review to understand state-of-art research related to learning preferences from the aspects of impacts, influential factors and evaluation methods. Design/methodology/approach This paper uses the systematic synthesis method to provide state-of-the-art knowledge on learning preference research by summarizing published studies in major databases and attempting to aggregate and reconcile the scientific results from the individual studies. The findings summarize aggregated research efforts and improve the quality of future research. Findings After analyzing existing literature, this study proposed three possible research directions in the future. First, researchers might focus on how to use the real-time tracking mechanism to further understand other impacts of learning preferences within the learning environments. Second, existing studies mainly focused on the influence of singular factors on learning preferences. The joint effects of multiple factors should be an important topic for future research. Finally, integrated algorithms might become the most popular evaluation method of learning preference in the era of smart learning environments. Research limitations/implications This review used the search results generated by Google Scholar and Web of Science databases. There might be published papers available in other databases that have not been taken into account. Originality/value The research summarizes the state-of-art research related to learning preferences. This paper is one of the first to discuss the development of learning preference research in smart learning environments.

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“…Context-awareness is defined as the process of detecting and analyzing context entities by various devices and analytical methods (Laine & Nygren, 2015). The context is multifaceted.…”

Section: Literature Reviewmentioning

confidence: 99%

“…”Learning context” has been defined as the set of entities that contribute to the learner’s situation (Laine & Nygren, 2015). Contextual entities in learning environments might include both external factors and internal factors (Hasanov et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

Learning preference: development in smart learning environments

Zhang

Hung

et al. 2021

IDD

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“…Recent development of advanced information technologies, such as wireless communications, sensors, and the Internet of Things, has enabled researchers to develop sophisticated adaptive and context-aware learning approaches. This paper uses a previous definition of context as a set of entities that constitutes the learner's situation [68]. Examples of these contextual entities in a learning environment are the learner's current location, time, other nearby learners, as well as the learner's personal learning style and learning history.…”

Section: Introductionmentioning

confidence: 99%

A survey of adaptive context-aware learning environments

Hasanov

Laine

Chung

2019

AIS

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Adaptive context-aware learning environments (ACALEs) can detect the learner's context and adapt learning materials to match the context. The support for context-awareness and adaptation is essential in these systems so that they can make learning contextually relevant. Previously, several related surveys have been conducted, but they are either outdated or they do not consider the important aspects of context-awareness, adaptation and pedagogy in the domain of ACALEs. To alleviate this, a comprehensive literature search on ACALEs was first performed. After filtering the results, 53 studies that were published between 2010 and 2018 were analyzed. The highlights of the results are: (i) mobile devices (PDAs, mobile phones, smartphones) are the most common client types, (ii) RFID/NFC are the most common sensors, (iii) ontology is the most common context modeling approach, (iv) context data typically originates from the learner profile or the learner's location, (v) rule-based adaptation is the most used adaptation mechanism, and (vi) informative feedback is the most common feedback type. Additionally, we conducted a trend analysis on technology usage in ACALEs throughout the covered timespan, and proposed a taxonomy of context categories as well as several other taxonomies for describing various aspects of ACALEs. Finally, based on the survey results, directions for future research in the field were given. These results can be of interest to educational technology researchers and to developers of adaptive and context-aware applications.

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Technology Integration in Next Generation Mobile Learning

Cited by 2 publications

References 1 publication

Learning preference: development in smart learning environments

Learning preference: development in smart learning environments

A survey of adaptive context-aware learning environments

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